AlphaFold 2 Monomer: Deployment in an HPC Environment

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Yang, Yuntao
Li, Zhao
Shih, David J. H.
Zheng, W. Jim

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AlphaFold2, developed by Google DeepMind, is a breakthrough in the grand challenge of protein structure prediction. While the breakthrough will have profound impact on biomedical research, its application faces significant hurdles due to the computing intensive nature. We overcome this challenge by deploying the AlphaFold 2 pipeline in an HPC environment that fully utilized the computing resources and accelerated the workflow. Specifically, the CPU component of the AlphaFold 2 that includes multiple sequence alignment and template search was deployed on a computer cluster at the Texas Advanced Computing Center (TACC). The high performance of CPU cores and I/O requests on the cluster allowed us to complete over 200 jobs within 10 hours. The GPU component that includes model prediction and refinement was deployed on the latest Nvidia GPU server, and 200 jobs could be completed within 24 hours when 2 jobs run in parallel. The deployed workflow can efficiently use different computing environments to process many protein structure predictions to advance biomedical research.



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